|Table of Contents|

[1] Gu Liang, Yang Peng, Dong Yongqiang,. A novel similarity measurement approachconsidering intrinsic user groups in collaborative filtering [J]. Journal of Southeast University (English Edition), 2015, 31 (4): 462-468. [doi:10.3969/j.issn.1003-7985.2015.04.006]

A novel similarity measurement approachconsidering intrinsic user groups in collaborative filtering()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2015 4
Research Field:
Information and Communication Engineering
Publishing date:


A novel similarity measurement approachconsidering intrinsic user groups in collaborative filtering
Gu Liang Yang Peng Dong Yongqiang
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 211189, China
similarity user group cluster collaborative filtering
To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups(SMCUG)is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the naïve k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, MovieLens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.


[1] Resnick P, Iacovou N, Suchak M, et al. GroupLens: an open architecture for collaborative filtering of netnews[C]//Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. Chapel Hill, NC, USA, 1994: 175-186.
[2] Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems, 2008, 16(1): 57-74.
[3] Hsu S H, Wen M H, Lin H C, et al. AIMED—a personalized TV recommendation system[M]//Interactive TV: a shared experience. Berlin: Springer, 2007: 166-174.
[4] Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
[5] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[6] Sahoo N, Singh P V, Mukhopadhyay T. A hidden Markov model for collaborative filtering[J/OL]. Management Information Systems Quarterly, 2012. http://ssrn.com/abstract=1700585.
[7] Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. Hong Kong, China, 2001: 285-295.
[8] Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Holland, 2007: 39-46.
[9] Kim H K, Kim J K, Ryu Y U. Personalized recommendation over a customer network for ubiquitous shopping[J]. IEEE Transactions on Services Computing, 2009, 2(2): 140-151.
[10] Choi K, Suh Y. A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-Based Systems, 2013, 37(2): 146-153.
[11] Sun H F, Chen J L, Yu G, et al. JacUOD: a new similarity measurement for collaborative filtering[J]. Journal of Computer Science and Technology, 2012, 27(6): 1252-1260.
[12] Bobadilla J, Ortega F, Hernando A. A collaborative filtering similarity measure based on singularities[J]. Information Processing & Management, 2012, 48(2): 204-217.
[13] Kaleli C. An entropy-based neighbor selection approach for collaborative filtering[J]. Knowledge-Based Systems, 2014, 56(3): 273-280.
[14] Dos Santos T R L, Zárate L E. Categorical data clustering: what similarity measure to recommend?[J]. Expert Systems with Applications, 2015, 42(3): 1247-1260.
[15] Song S, Zhu H, Chen L. Probabilistic correlation-based similarity measure on text records[J]. Information Sciences, 2014, 289(5): 8-24.
[16] Jiang Y, Wang X, Zheng H T. A semantic similarity measure based on information distance for ontology alignment[J]. Information Sciences, 2014, 278(10): 76-87.
[17] Xue G R, Lin C, Yang Q, et al. Scalable collaborative filtering using cluster-based smoothing[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Singapore, 2005: 114-121.
[18] Roh T H, Oh K J, Han I. The collaborative filtering recommendation based on SOM cluster-indexing CBR[J]. Expert Systems with Applications, 2003, 25(3): 413-423.
[19] Honda K, Sugiura N, Ichihashi H, et al. Collaborative filtering using principal component analysis and fuzzy clustering[M]//Web intelligence: research and development. Berlin: Springer, 2001: 394-402.
[20] Bilge A, Polat H. A comparison of clustering-based privacy-preserving collaborative filtering schemes[J]. Applied Soft Computing, 2013, 13(5): 2478-2489.
[21] Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values[J]. Data Mining & Knowledge Discovery, 1998, 2(3): 283-304.


Biographies: Gu Liang(1989—), male, graduate; Yang Peng(corresponding author), male, doctor, associate professor, pengyang@seu.edu.cn.
Foundation items: The National High Technology Research and Development Program of China(863 Program)(No.2013AA013503), the National Natural Science Foundation of China(No.61472080, 61370206, 61300200), the Consulting Project of Chinese Academy of Engineering(No.2015-XY-04), the Foundation of Collaborative Innovation Center of Novel Software Technology and Industrialization.
Citation: Gu Liang, Yang Peng, Dong Yongqiang. A novel similarity measurement approach considering intrinsic user groups in collaborative filtering[J].Journal of Southeast University(English Edition), 2015, 31(4):462-468.[doi:10.3969/j.issn.1003-7985.2015.04.006]
Last Update: 2015-12-20